build: bump version 0.7.4 → 0.7.5

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.7.3
+current_version = 0.7.5
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)

docs/source/conf.py

@@ -23,7 +23,7 @@ author = "Jensun Ravichandran"
 
 # The full version, including alpha/beta/rc tags
 #
-release = "0.7.3"
+release = "0.7.5"
 
 # -- General configuration ---------------------------------------------------
 

examples/cbc_iris.py

@@ -1,5 +1,7 @@
 """ProtoTorch CBC example using 2D Iris data."""
 
+import logging
+
 import torch
 from matplotlib import pyplot as plt
 
@@ -34,7 +36,7 @@ class VisCBC2D():
         self.resolution = 100
         self.cmap = "viridis"
 
-    def on_epoch_end(self):
+    def on_train_epoch_end(self):
         x_train, y_train = self.x_train, self.y_train
         _components = self.model.components_layer._components.detach()
         ax = self.fig.gca()
@@ -94,5 +96,5 @@ if __name__ == "__main__":
             correct += (y_pred.argmax(1) == y).float().sum(0)
 
         acc = 100 * correct / len(train_ds)
-        print(f"Epoch: {epoch} Accuracy: {acc:05.02f}%")
-        vis.on_epoch_end()
+        logging.info(f"Epoch: {epoch} Accuracy: {acc:05.02f}%")
+        vis.on_train_epoch_end()

prototorch/__init__.py

@@ -17,7 +17,7 @@ from .core import similarities  # noqa: F401
 from .core import transforms  # noqa: F401
 
 # Core Setup
-__version__ = "0.7.3"
+__version__ = "0.7.5"
 
 __all_core__ = [
     "competitions",

prototorch/core/competitions.py

@@ -38,7 +38,7 @@ def cbcc(detections: torch.Tensor, reasonings: torch.Tensor):
     pk = A
     nk = (1 - A) * B
     numerator = (detections @ (pk - nk).T) + nk.sum(1)
-    probs = numerator / (pk + nk).sum(1)
+    probs = numerator / ((pk + nk).sum(1) + 1e-8)
     return probs
 
 

prototorch/datasets/sklearn.py

@@ -5,8 +5,10 @@ URL:
 
 """
 
+from __future__ import annotations
+
 import warnings
-from typing import Sequence, Union
+from typing import Sequence
 
 from sklearn.datasets import (
     load_iris,
@@ -41,9 +43,9 @@ class Iris(NumpyDataset):
     :param dims: select a subset of dimensions
     """
 
-    def __init__(self, dims: Sequence[int] = None):
+    def __init__(self, dims: Sequence[int] | None = None):
         x, y = load_iris(return_X_y=True)
-        if dims:
+        if dims is not None:
             x = x[:, dims]
         super().__init__(x, y)
 
@@ -56,15 +58,19 @@ class Blobs(NumpyDataset):
 
     """
 
-    def __init__(self,
-                 num_samples: int = 300,
-                 num_features: int = 2,
-                 seed: Union[None, int] = 0):
-        x, y = make_blobs(num_samples,
-                          num_features,
-                          centers=None,
-                          random_state=seed,
-                          shuffle=False)
+    def __init__(
+        self,
+        num_samples: int = 300,
+        num_features: int = 2,
+        seed: None | int = 0,
+    ):
+        x, y = make_blobs(
+            num_samples,
+            num_features,
+            centers=None,
+            random_state=seed,
+            shuffle=False,
+        )
         super().__init__(x, y)
 
 
@@ -77,29 +83,33 @@ class Random(NumpyDataset):
     Note: n_classes * n_clusters_per_class <= 2**n_informative must satisfy.
     """
 
-    def __init__(self,
-                 num_samples: int = 300,
-                 num_features: int = 2,
-                 num_classes: int = 2,
-                 num_clusters: int = 2,
-                 num_informative: Union[None, int] = None,
-                 separation: float = 1.0,
-                 seed: Union[None, int] = 0):
+    def __init__(
+        self,
+        num_samples: int = 300,
+        num_features: int = 2,
+        num_classes: int = 2,
+        num_clusters: int = 2,
+        num_informative: None | int = None,
+        separation: float = 1.0,
+        seed: None | int = 0,
+    ):
         if not num_informative:
             import math
             num_informative = math.ceil(math.log2(num_classes * num_clusters))
             if num_features < num_informative:
                 warnings.warn("Generating more features than requested.")
                 num_features = num_informative
-        x, y = make_classification(num_samples,
-                                   num_features,
-                                   n_informative=num_informative,
-                                   n_redundant=0,
-                                   n_classes=num_classes,
-                                   n_clusters_per_class=num_clusters,
-                                   class_sep=separation,
-                                   random_state=seed,
-                                   shuffle=False)
+        x, y = make_classification(
+            num_samples,
+            num_features,
+            n_informative=num_informative,
+            n_redundant=0,
+            n_classes=num_classes,
+            n_clusters_per_class=num_clusters,
+            class_sep=separation,
+            random_state=seed,
+            shuffle=False,
+        )
         super().__init__(x, y)
 
 
@@ -113,16 +123,20 @@ class Circles(NumpyDataset):
 
     """
 
-    def __init__(self,
-                 num_samples: int = 300,
-                 noise: float = 0.3,
-                 factor: float = 0.8,
-                 seed: Union[None, int] = 0):
-        x, y = make_circles(num_samples,
-                            noise=noise,
-                            factor=factor,
-                            random_state=seed,
-                            shuffle=False)
+    def __init__(
+        self,
+        num_samples: int = 300,
+        noise: float = 0.3,
+        factor: float = 0.8,
+        seed: None | int = 0,
+    ):
+        x, y = make_circles(
+            num_samples,
+            noise=noise,
+            factor=factor,
+            random_state=seed,
+            shuffle=False,
+        )
         super().__init__(x, y)
 
 
@@ -136,12 +150,16 @@ class Moons(NumpyDataset):
 
     """
 
-    def __init__(self,
-                 num_samples: int = 300,
-                 noise: float = 0.3,
-                 seed: Union[None, int] = 0):
-        x, y = make_moons(num_samples,
-                          noise=noise,
-                          random_state=seed,
-                          shuffle=False)
+    def __init__(
+        self,
+        num_samples: int = 300,
+        noise: float = 0.3,
+        seed: None | int = 0,
+    ):
+        x, y = make_moons(
+            num_samples,
+            noise=noise,
+            random_state=seed,
+            shuffle=False,
+        )
         super().__init__(x, y)

prototorch/datasets/tecator.py

@@ -36,6 +36,7 @@ Description:
     are determined by analytic chemistry.
 """
 
+import logging
 import os
 
 import numpy as np
@@ -81,13 +82,11 @@ class Tecator(ProtoDataset):
         if self._check_exists():
             return
 
-        if self.verbose:
-            print("Making directories...")
+        logging.debug("Making directories...")
         os.makedirs(self.raw_folder, exist_ok=True)
         os.makedirs(self.processed_folder, exist_ok=True)
 
-        if self.verbose:
-            print("Downloading...")
+        logging.debug("Downloading...")
         for fileid, md5 in self._resources:
             filename = "tecator.npz"
             download_file_from_google_drive(fileid,
@@ -95,8 +94,7 @@ class Tecator(ProtoDataset):
                                             filename=filename,
                                             md5=md5)
 
-        if self.verbose:
-            print("Processing...")
+        logging.debug("Processing...")
         with np.load(os.path.join(self.raw_folder, "tecator.npz"),
                      allow_pickle=False) as f:
             x_train, y_train = f["x_train"], f["y_train"]
@@ -117,5 +115,4 @@ class Tecator(ProtoDataset):
                   "wb") as f:
             torch.save(test_set, f)
 
-        if self.verbose:
-            print("Done!")
+        logging.debug("Done!")

setup.py

@@ -20,9 +20,9 @@ with open("README.md", "r") as fh:
 
 INSTALL_REQUIRES = [
     "torch>=1.3.1",
-    "torchvision>=0.7.3",
+    "torchvision>=0.7.5",
     "numpy>=1.9.1",
-    "sklearn",
+    "scikit-learn",
     "matplotlib",
 ]
 DATASETS = [
@@ -51,7 +51,7 @@ ALL = DATASETS + DEV + DOCS + EXAMPLES + TESTS
 
 setup(
     name="prototorch",
-    version="0.7.3",
+    version="0.7.5",
     description="Highly extensible, GPU-supported "
     "Learning Vector Quantization (LVQ) toolbox "
     "built using PyTorch and its nn API.",